IL-37: a new anti-inflammatory cytokine of the IL-1 family
Bibliographic record
Abstract
The IL-1 family of cytokines encompasses eleven proteins that each share a similar β-barrel structure and bind to Ig-like receptors. Some of the IL-1-like cytokines have been well characterised, and play key roles in the development and regulation of inflammation. Indeed, IL-1α (IL-1F1), IL-1β (IL-1F2), and IL-18 (IL-1F4) are well-known inflammatory cytokines active in the initiation of the inflammatory reaction and in driving Th1 and Th17 inflammatory responses. In contrast, IL-1 receptor antagonist (IL-1Ra, IL-1F3) and the receptor antagonist binding to IL-1Rrp2 (IL-36Ra, IL-1F5) reduce inflammation by blocking the binding of the agonist receptor ligands. In the case of IL-37 (IL-1F7), of which five different splice variants have been described, less is known of its function, and identification of the components of a heterodimeric receptor complex remains unclear. Some studies suggest that IL-37 binds to the α chain of the IL-18 receptor in a non-competitive fashion, and this may explain some of the disparate biological effects that have been reported for mice deficient in the IL-18R. The biological properties of IL-37 are mainly those of down-regulating inflammation, as assessed in models where human IL-37 is expressed in mice. In this review, an overview of the role of IL-37 in the regulation of inflammation is presented. The finding that IL-37 also locates to the nucleus, as do IL-1α and IL-33, for receptor-independent organ/tissue-specific regulation of inflammation is also reviewed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".